Oversampling is a proven technique for combating class imbalance for traditional (i.e., shallow) machine learning models [1]}. Three leading over-sampling methods are: Synthetic Minority Over-Sampling (SMOTE) [2]}, Border-line SMOTE [3]}, and Balanced SVM [4]}. All three methods are interpolative in the sense that they generate new samples by randomly inserting data points between minority class samples.
